With the increasing penetration of photovoltaic （PV） generation into power systems，the randomness and uncertainty of its output have raised higher requirements for the flexible peak regulation capability of grids. To offer more accurate predicted scenarios and facilitate flexibility oriented dispatch，an intelligent method integrating fuzzy clustering，similar day extraction，and probabilistic prediction was developed. Highly correlated meteorological variables including temperature，humidity，global horizontal irradiance，and tilted irradiance were first identified using the Pearson correlation coefficient. Fuzzy C-means （FCM） clustering was then applied to classify weather types. Feature weights were determined using the CRITIC method，and similar days within each weather category were extracted based on weighted Euclidean distance to construct a high quality training dataset. A quantile regression long short-term memory （QRLSTM） network was subsequently employed to perform short-term probabilistic forecasting of PV output. Simulation results demonstrated that the proposed approach achieved high prediction accuracy across various weather conditions，with confidence interval coverage rates exceeding 90% and significantly reduced confidence interval ranges compared to those of benchmark models. It was concluded that the proposed method effectively enhances the reliability and robustness of PV power prediction and provides high quality scenario support for uncertainty aware dispatch in multi-energy complementary systems.